Aarhus University Seal

Forecasting day-ahead natural gas demand in Denmark

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Natural gas demand forecasting is important for all players in the natural gas market. This work compares four possible day ahead natural gas consumption forecasting models in order to forecast the natural gas consumption of the four subnets in Denmark. The forecasts from the suggested model were used to regulate the linepack of the pipelines, which provides the stability and security of the natural gas transmission system. A detailed variable analysis, analysis of the exogenous variable error, and combination forecasts in order to maximize the forecasting accuracy are presented here. With the proposed models, a reduction in error, ranging from 34% to 72%, was achieved for each subnet in comparison to the current Energinet forecaster. Additionally, compared to a univariate model, the data rich models showed 20%–47% lower error. It was also seen that the exogenous variable error was negligible in comparison to the benefit of using variable rich models. Contrary to some of the recent studies, solar radiation was found ineffective in terms of predictive accuracy for the used data sets.

Original languageEnglish
Article number103193
JournalJournal of Natural Gas Science & Engineering
Number of pages25
Publication statusPublished - Apr 2020

    Research areas

  • Artificial neural networks, Combination forecasts, Day ahead forecasting, Natural gas consumption forecasting, Variable analysis

See relations at Aarhus University Citationformats

ID: 181272336